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Channel estimation method based on Bayesian algorithm

A Bayesian algorithm and channel estimation technology, which is applied in the field of wireless communication, can solve problems such as large training costs, channel estimation meaninglessness, and long training time, so as to simplify the calculation amount, improve the calculation speed and calculation accuracy, and improve the accuracy. sexual effect

Inactive Publication Date: 2017-08-22
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

In this case, the number of pilot signals is proportional to the number of base station antennas. Due to the huge number of antennas in massive MIMO systems, conventional channel estimation methods (such as the least squares method) will face huge training overhead, making the training time become shorter. Long, even exceeding the channel's dry time, making channel estimation meaningless

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  • Channel estimation method based on Bayesian algorithm
  • Channel estimation method based on Bayesian algorithm
  • Channel estimation method based on Bayesian algorithm

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Embodiment

[0042] figure 2 It is a flow chart of multi-user massive MIMO channel estimation. According to the flow chart, taking the above parameters as an example, this example specifically includes:

[0043] S1. Initialization, specifically:

[0044] S11. The BS uses G time slots to broadcast G multi-task pilot signals H=[H 1 ,H 2 ,H3 ]∈C 50×3 , where H is sparse p =[h 1 ,h 2 ,..., h 50 ] T , multitasking channel H p have the same sparse properties.

[0045] S12. The receiving signal matrix of the multi-tasking at the user end is R=[R 1 , R 2 , R 3 ] where R p Represents the received signal matrix of the pth task, p=1,2,3.

[0046] S2. Multi-task sparse support joint estimation, that is, use the Bayesian algorithm to jointly estimate multi-task signals, and obtain the mean value u of each position element p (m) and variance Σ p (m). The specific iterative estimation algorithm is as follows:

[0047] S21. Set the iteration control variable ε=10 supported by P task sig...

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Abstract

The invention belongs to the technical field of wireless communication, and specifically relates to a channel estimation method based on the Bayesian algorithm. According to the method provided by the invention, a shared variance parameter of a shared sparse position is calculation in junction with the Bayesian algorithm, compared with the ordinary Bayesian variance parameter estimation method, the accuracy of variance estimation is greatly improved, and meanwhile the Bayesian algorithm is replaced by the GAMP algorithm to avoid a direct matrix inversion process of a posterior probability. Compared with the traditional method, the method provided by the invention has the beneficial effects of simplifying the calculated amount, improves the operation speed and the operation precision, and improves the channel estimation accuracy.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a channel estimation method based on a Bayesian algorithm. Background technique [0002] Massive MIMO (Multiple Input Multiple Output) system is one of the key technologies of the fifth-generation mobile communication system. Its main advantages are: system capacity increases with the number of antennas; transmission signal power is reduced; simple The linear precoder and detector can achieve the optimal performance; the channels tend to be orthogonal, so the co-channel interference in the cell is eliminated. The prerequisite for realizing these advantages is that the base station (BS) knows the channel state information (CSIT). In a Time Division Duplex (TDD) system, channel estimation is performed at the user end (MS) by utilizing the reciprocity of the uplink and downlink channels. For the FDD massive MIMO system, the channel estimation process is ...

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Application Information

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IPC IPC(8): H04L25/02H04B17/391H04B7/0413
CPCH04B7/0413H04B17/3911H04L25/024
Inventor 孙晶晶成先涛
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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